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- Creators: Davulcu, Hasan
- Member of: ASU Electronic Theses and Dissertations
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
A semiconductor supply chain modeling and simulation platform using Linear Program (LP) optimization and parallel Discrete Event System Specification (DEVS) process models has been developed in a joint effort by ASU and Intel Corporation. A Knowledge Interchange Broker (KIBDEVS/LP) was developed to broker information synchronously between the DEVS and LP models. Recently a single-echelon heuristic Inventory Strategy Module (ISM) was added to correct for forecast bias in customer demand data using different smoothing techniques. The optimization model could then use information provided by the forecast model to make better decisions for the process model. The composition of ISM with LP and DEVS models resulted in the first realization of what is now called the Optimization Simulation Forecast (OSF) platform. It could handle a single echelon supply chain system consisting of single hubs and single products In this thesis, this single-echelon simulation platform is extended to handle multiple echelons with multiple inventory elements handling multiple products. The main aspect for the multi-echelon OSF platform was to extend the KIBDEVS/LP such that ISM interactions with the LP and DEVS models could also be supported. To achieve this, a new, scalable XML schema for the KIB has been developed. The XML schema has also resulted in strengthening the KIB execution engine design. A sequential scheme controls the executions of the DEVS-Suite simulator, CPLEX optimizer, and ISM engine. To use the ISM for multiple echelons, it is extended to compute forecast customer demands and safety stocks over multiple hubs and products. Basic examples for semiconductor manufacturing spanning single and two echelon supply chain systems have been developed and analyzed. Experiments using perfect data were conducted to show the correctness of the OSF platform design and implementation. Simple, but realistic experiments have also been conducted. They highlight the kinds of supply chain dynamics that can be evaluated using discrete event process simulation, linear programming optimization, and heuristics forecasting models.
ContributorsSmith, James Melkon (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Davulcu, Hasan (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
The complexity of the systems that software engineers build has continuously grown since the inception of the field. What has not changed is the engineers' mental capacity to operate on about seven distinct pieces of information at a time. The widespread use of UML has led to more abstract software design activities, however the same cannot be said for reverse engineering activities. The introduction of abstraction to reverse engineering will allow the engineer to move farther away from the details of the system, increasing his ability to see the role that domain level concepts play in the system. In this thesis, we present a technique that facilitates filtering of classes from existing systems at the source level based on their relationship to concepts in the domain via a classification method using machine learning. We showed that concepts can be identified using a machine learning classifier based on source level metrics. We developed an Eclipse plugin to assist with the process of manually classifying Java source code, and collecting metrics and classifications into a standard file format. We developed an Eclipse plugin to act as a concept identifier that visually indicates a class as a domain concept or not. We minimized the size of training sets to ensure a useful approach in practice. This allowed us to determine that a training set of 7:5 to 10% is nearly as effective as a training set representing 50% of the system. We showed that random selection is the most consistent and effective means of selecting a training set. We found that KNN is the most consistent performer among the learning algorithms tested. We determined the optimal feature set for this classification problem. We discussed two possible structures besides a one to one mapping of domain knowledge to implementation. We showed that classes representing more than one concept are simply concepts at differing levels of abstraction. We also discussed composite concepts representing a domain concept implemented by more than one class. We showed that these composite concepts are difficult to detect because the problem is NP-complete.
ContributorsCarey, Maurice (Author) / Colbourn, Charles (Thesis advisor) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This is a study of scientific realism, and of the extent to which it is undermined by objections that have been raised by advocates of various forms of antirealism. I seek to develop and present a version of scientific realism that improves on past formulations, and then to show that standard antirealist arguments against it do not succeed. In this paper, I will first present my formulation of scientific realism, which conceives of theories as model-based and as fundamentally non-linguistic. I advocate an epistemic position that accords with indirect realism, and I review and assess the threat posed by theses of underdetermination. Next, I review and discuss three important views: the antirealist constructivist view of Thomas Kuhn, the realist view of Norwood Hanson, and the antirealist constructive empiricist view of Bas van Fraassen. I find merits and flaws in all three views. In the course of those discussions, I develop the theme that antirealists' arguments generally depend on assumptions that are open to question, especially from the perspective of the version of realism I advocate. I further argue that these antirealist views are undermined by their own tacit appeals to realism.
ContributorsNovack, Alexander Dion (Author) / Armendt, Brad (Thesis advisor) / Creath, Richard (Committee member) / French, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
Text classification, in the artificial intelligence domain, is an activity in which text documents are automatically classified into predefined categories using machine learning techniques. An example of this is classifying uncategorized news articles into different predefined categories such as "Business", "Politics", "Education", "Technology" , etc. In this thesis, supervised machine learning approach is followed, in which a module is first trained with pre-classified training data and then class of test data is predicted. Good feature extraction is an important step in the machine learning approach and hence the main component of this text classifier is semantic triplet based features in addition to traditional features like standard keyword based features and statistical features based on shallow-parsing (such as density of POS tags and named entities). Triplet {Subject, Verb, Object} in a sentence is defined as a relation between subject and object, the relation being the predicate (verb). Triplet extraction process, is a 5 step process which takes input corpus as a web text document(s), each consisting of one or many paragraphs, from RSS feeds to lists of extremist website. Input corpus feeds into the "Pronoun Resolution" step, which uses an heuristic approach to identify the noun phrases referenced by the pronouns. The next step "SRL Parser" is a shallow semantic parser and converts the incoming pronoun resolved paragraphs into annotated predicate argument format. The output of SRL parser is processed by "Triplet Extractor" algorithm which forms the triplet in the form {Subject, Verb, Object}. Generalization and reduction of triplet features is the next step. Reduced feature representation reduces computing time, yields better discriminatory behavior and handles curse of dimensionality phenomena. For training and testing, a ten- fold cross validation approach is followed. In each round SVM classifier is trained with 90% of labeled (training) data and in the testing phase, classes of remaining 10% unlabeled (testing) data are predicted. Concluding, this paper proposes a model with semantic triplet based features for story classification. The effectiveness of the model is demonstrated against other traditional features used in the literature for text classification tasks.
ContributorsKarad, Ravi Chandravadan (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The academic literature on science communication widely acknowledges a problem: science communication between experts and lay audiences is important, but it is not done well. General audience popular science books, however, carry a reputation for clear science communication and are understudied in the academic literature. For this doctoral dissertation, I utilize Sam Harris's The Moral Landscape, a general audience science book on the particularly thorny topic of neuroscientific approaches to morality, as a case-study to explore the possibility of using general audience science books as models for science communication more broadly. I conduct a literary analysis of the text that delimits the scope of its project, its intended audience, and the domains of science to be communicated. I also identify seven literary aspects of the text: three positive aspects that facilitate clarity and four negative aspects that interfere with lay public engagement. I conclude that The Moral Landscape relies on an assumed knowledge base and intuitions of its audience that cannot reasonably be expected of lay audiences; therefore, it cannot properly be construed as popular science communication. It nevertheless contains normative lessons for the broader science project, both in literary aspects to be salvaged and literary aspects and concepts to consciously be avoided and combated. I note that The Moral Landscape's failings can also be taken as an indication that typical descriptions of science communication offer under-detailed taxonomies of both audiences for science communication and the varieties of science communication aimed at those audiences. Future directions of study include rethinking appropriate target audiences for science literacy projects and developing a more discriminating taxonomy of both science communication and lay publics.
ContributorsJohnson, Nathan W (Author) / Robert, Jason S (Thesis advisor) / Creath, Richard (Committee member) / Martinez, Jacqueline (Committee member) / Sylvester, Edward (Committee member) / Lynch, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
There has been a lot of research in the field of artificial intelligence about thinking machines. Alan Turing proposed a test to observe a machine's intelligent behaviour with respect to natural language conversation. The Winograd schema challenge is suggested as an alternative, to the Turing test. It needs inferencing capabilities, reasoning abilities and background knowledge to get the answer right. It involves a coreference resolution task in which a machine is given a sentence containing a situation which involves two entities, one pronoun and some more information about the situation and the machine has to come up with the right resolution of a pronoun to one of the entities. The complexity of the task is increased with the fact that the Winograd sentences are not constrained by one domain or specific sentence structure and it also contains a lot of human proper names. This modification makes the task of association of entities, to one particular word in the sentence, to derive the answer, difficult. I have developed a pronoun resolver system for the confined domain Winograd sentences. I have developed a classifier or filter which takes input sentences and decides to accept or reject them based on a particular criteria. Once the sentence is accepted. I run parsers on it to obtain the detailed analysis. Furthermore I have developed four answering modules which use world knowledge and inferencing mechanisms to try and resolve the pronoun. The four techniques I use are : ConceptNet knowledgebase, Search engine pattern counts,Narrative event chains and sentiment analysis. I have developed a particular aggregation mechanism for the answers from these modules to arrive at a final answer. I have used caching technique for the association relations that I obtain for different modules, so as to boost the performance. I run my system on the standard ‘nyu dataset’ of Winograd sentences and questions. This dataset is then restricted, by my classifier, to 90 sentences. I evaluate my system on this 90 sentence dataset. When I compare my results against the state of the art system on the same dataset, I get nearly 4.5 % improvement in the restricted domain.
ContributorsBudukh, Tejas Ulhas (Author) / Baral, Chitta (Thesis advisor) / VanLehn, Kurt (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Once perceived as an unimportant occurrence in living organisms, cell degeneration was reconfigured as an important biological phenomenon in development, aging, health, and diseases in the twentieth century. This dissertation tells a twentieth-century history of scientific investigations on cell degeneration, including cell death and aging. By describing four central developments in cell degeneration research with the four major chapters, I trace the emergence of the degenerating cell as a scientific object, describe the generations of a variety of concepts, interpretations and usages associated with cell death and aging, and analyze the transforming influences of the rising cell degeneration research. Particularly, the four chapters show how the changing scientific practices about cellular life in embryology, cell culture, aging research, and molecular biology of Caenorhabditis elegans shaped the interpretations about cell degeneration in the twentieth-century as life-shaping, limit-setting, complex, yet regulated. These events created and consolidated important concepts in life sciences such as programmed cell death, the Hayflick limit, apoptosis, and death genes. These cases also transformed the material and epistemic practices about the end of cellular life subsequently and led to the formations of new research communities. The four cases together show the ways cell degeneration became a shared subject between molecular cell biology, developmental biology, gerontology, oncology, and pathology of degenerative diseases. These practices and perspectives created a special kind of interconnectivity between different fields and led to a level of interdisciplinarity within cell degeneration research by the early 1990s.
ContributorsJiang, Lijing (Author) / Maienschein, Jane (Thesis advisor) / Laubichler, Manfred (Thesis advisor) / Hurlbut, James (Committee member) / Creath, Richard (Committee member) / White, Michael (Committee member) / Arizona State University (Publisher)
Created2013